This is probably the best online debate of all time on poverty measurement! It’s particularly exciting because as I said in my post this is very much a live debate within the World Bank. What do you think? How is the MPI perceived in your country? Should World Bank poverty economists calculate and promote the MPI?

Comments

Thanks for this update. More on this can be found on 'Free Exchange' (The Economist). Link- http://www.economist.com/blogs/freeexchange/2010/08/measuring_povertyhihi. The NPR blog has also covered this discussion- http://www.npr.org/blogs/money/2010/08/04/128968222/a-new-way-to-measure-poverty-proves-controversial

Gabriel, Hope you're well.
Fascinating discussion at so many levels. Thanks for the round-up!
Coming from the perspective of Nuru International (http://www.nuruinternational.org/), a small NGO working to end (and measure) extreme poverty, I have been surprised at how little discussion I have seen about the Multidimensional Poverty Assessment Tool released by IFAD a few months ago (http://www.ifad.org/mpat/) . Granted, the focus of the MPAT is different from the MPI – its focus is community level data collection to measure a community’s poverty level (and thus see what poverty programs are working and where) rather than creating a different way to look at national level data that already exists. Different – but perhaps both necessary? Would love to hear some analysis and critiques on the MPAT as well. Is this a tool that World Bank poverty economists and others would recommend for NGOs working at the community level?

Hi, Stephanie,
I took a look at the MPAT materials. Certainly, the MPAT approach--which consists of a community-level survey and then producing sector-specific indicators--avoids the pitfall of the MPI of trying to wrap everything together into one Ultimate Indicator. The design seems to be well done. It's hard for me to judge, however, how useful the MPAT would be for tracking what's happening with specific interventions. It would depend on what particular interventions were involved, and whether they matched up with the MPAT components. I can imagine, say, an NGO working on rural agriculture in a few communities; would it really make sense for the NGO to spend time collecting data on school quality? Possibly, but it would depend on the specifics. There's a tradeoff between having a general tool that works for many cases vs. tailor-making evaluation tools to specific situations. Anyway, thanks for alerting me to this tool.

This very interesting debate seems to boil down to whether or not it is valid and useful to promulgate a single numerical index to try to represent and quantify the multi-dimensional nature of poverty (secondary questions relate to how best to do so if the answer is yes). Whatever the theoretical pros and cons I think the reality is that we use such "simple" indexes all the time both because we have become used to getting our information in sound-bite form and because at the end of the day there are times when you need a single metric for decision-making purposes. Witness the widespread use of GDP, which everyone agrees is grossly inadequate and misleading since it's typically regarded by most people as a measure of wealth and well-being rather than as a measure of economic activity which can reflect motion without progress (the classic cases of being in a car accident, or digging a hole and refilling it both adding to GDP). So the "simple" index is a fact of life, and it's better to at least try to have it capture non-income related aspects, however imperfectly. While it may be a ridiculous proposition to compare the weight of the life (or death) of a child with that of an extra year of schooling, it's even more ridiculous to act on the assumption that the value of each is zero.

A Composite index can be useful for global and aggregated comparisons, and it needs to be restricted to only few indicators because it is constrained by the common available data across countries. However at the country level, measuring poverty should be country specific. In general terms there are some common variables that can be applied to every country, such as education, health, access to basic services, appliances, etc. The way these variables affect welfare of households in each country is different. In the same way that there are different methodologies to measure poverty, there are different methodologies to find the poor, which is even more challenging. The experience from some LAC countries (such as Chile, Colombia, Costa Rica and Mexico among others) in the last 30 years has shown that using representative household surveys, is possible to estimate good PMT (Proxy Means Test) models to estimate either consumption or income of the households. These PMT models are indeed multidimensional indexes that in most cases incorporate many relevant variables to measure "welfare". The variables that can be included in this multidimensional index depend on what the household surveys have. In most cases household surveys include the different dimensions of poverty or welfare and the last generation of household surveys includes also vulnerability variables. This methodology is useful because it allows converting the welfare to numbers. Converting all the dimension of welfare to numbers or money terms allows to do a standardized welfare assessment. Whit this methodology is possible to select the poor comparing the welfare of each household to the existing cut-offs of the country or the estimated ones. If this methodology, is "well implemented", it is useful because it allows to have aggregated information about poverty in the country. At the same time, it also has the detailed information of who the poor are. This will help to focus and direct the social policies to them. That, I think is a good approach to measuring poverty with the focus of helping the poor. The PMT methodology can be applied best to countries where the share of the informal sector is high in the labor market. In countries, where informality is not so high and there are good mechanisms to verify income, then means test is a better alternative.

The multi-dimensionality of an index of human development, of poverty, or of any other social phenomenon, is always a welcome approach, since it takes into account factors that affect the well-being of the population. The High Commission for Planning (HCP), itself, developed in 2008 a multidimensional approach of poverty, remaining convinced, however, that the choices that such approaches imply remain debatable (see Annex 1). The HCP Standard of Living Index ( INV), developed by the High Commission for Planning on the basis of a data base collected via surveys on households’ standard of living and consumption (see annex). The INV measures multidimensional poverty. It covers access to education, health (medical-health coverage and consultation), healthy and balanced nutrition (drinking water availability, decent nutrition (according to the WHO-FAO standards) and sustainable self-protection from food poverty), housing conditions (decent housing, equipped with electricity, liquid sanitation, refrigerator, bath/shower, kitchen, toilet and stove), vocational integration represented by the economic activity of the household’s members and employment opportunities for young people, social equity and gender equality, respectively measured by the position in the social ladder of the standard of living and gender equality in terms of education- training, health care, and access to means of communication and transportation (see www.omdh.hcp.ma).
As for Oxford Poverty and Human Development Initiative Center (OPHI), it has developed a multidimensional poverty index (MPI) published in a report entitled "Acute Multidimensional Poverty a new index for developing countries (see Annex 2).
This work was authored by Sabina Alkire and Maria Emma Santos of the OPHI Research Centre, on the basis of an approach designed in 2007 by James Foster and Sabina Alkire. Tailored on the basis of demography and health surveys (DHS), aiming to analyze health situation rather than the extent of poverty, the OPHI index is constrained by data limitations, as admitted by its own authors (page 7 of the Report), and faces, according to WB experts (www.oxfamblogs.org), empirical and analytical limitations (see Appendix 5). It raises the following comments:
1. The dimensions included in the calculation of the Multidimensional Poverty Index are, by their multiplicity, certainly advantageous. Still, they do not cover all socio-economic priorities, including those that develop the
capability of individuals to self-protect themselves from poverty. They are limited to ten indicators pertaining to health (infant mortality and nutrition), basic education and access to electricity, drinking water, sanitation, and some household durables (see Annex 2). Their choice was dictated by constraints linked much more to the nature of data collected by the Demography and Health Surveys than to the priorities and aspirations of the population. Besides, only output indicators are identified by this index. The input indicators that measure the ability of the population to be self-sustained are all excluded. This applies to income factors such as employment, social security insurance or access to road network and financing means.
2. The variables on which the measurement of the Multidimensional Poverty Index is based become problematic when poverty is put in a dynamic approach. For example, a malnourished child contributes with 1.67 to the value of the household deprivations. If this child is aged between 6 and 14 years and has never attended school, the value of household deprivation shall increase by 1.67. Thus, it amounts to a total of 3.34 (1.67 + 1.67). The approach does not assign this value (3.34) to the child alone but to all those who live with him under the same roof. Since this value (3.34) exceeds the poverty line, the value of which is arbitrarily fixed at 3, the child and the rest of the household members are all classified as poor, regardless the resources they have. Furthermore, when this child is 15 years old, he shall no longer be concerned by the variable of "Education of children between 6 and 14 years." That is to say that his non-education is no longer sanctioned by an increase, of 1.67, of the value of the household deprivation. This value is then reduced to 1.67 (3.34 - 1.67), below the poverty line, and, therefore, the whole household shall no longer be classified as poor. Such approach does not allow us, in any case, to understand the dynamics of poverty, nor to analyze its determinants.
3. Like all multidimensional approaches, the measurement of poverty according to the Multidimensional Poverty Index is based on a subjective threshold and does not take into consideration, therefore, comparisons with the monetary approach, the threshold of which is determined objectively. In
addition to the aforesaid limitations, there is the subjective nature of the poverty line, arbitrarily set at 30%, admitted by the MPI authors themselves. This makes groundless any comparison of poverty rates according to the MPI with those based on monetary approaches. Indeed, 23 among the 55 countries, better ranked than Morocco, have a poverty rate of U.S. $ 2 PPP significantly higher than the Moroccan poverty rate. Similarly, 18 countries better ranked than Morocco have a level of inequality higher than Morocco’s.
4. Data reference periods range from 2000 to 2008, making the classification of countries according to the Multidimensional Poverty Index groundless. The reference years of surveys/data sources differ from one country to another and do not, in any way, classify countries according to this index level. For instance, the adopted classification compares Morocco in 2004 to Egypt in 2008 and Jordan in 2007. All efforts made by Morocco, between 2004 and 2008, in terms of human, economic and social development and also in terms of production of current statistics are thus omitted for the simple reason that the last Demography and Health Survey conducted in Morocco is dated 2004. The 2009 MDG national report provides recent data on the basic dimensions of the Multidimensional Poverty Index. It underlines that the new data sources available in Morocco (the Demographic Survey of 2009 and the survey on the standard of living 2007) allow to update this index.
In terms of data availability, only 61 among the 104 countries concerned have data on the 10 used indicators. For the remaining countries, the missing indicators are replaced by the, lower or higher, bounds of variables or their proxies.
5. The application, itself, of the OPHI multidimensional poverty approach to Moroccan data gives similar results to those calculated by the HCP approach (see table in Annex 3). The OPHI approach applied to Moroccan survey data shows that poverty declined from 28.5% in 2004 to 11.1% in 2007.
According to the HCP multidimensional approach, poverty declined from 23.9% in 2001 to 12.1% in 2007. This means that the decline in poverty rates is confirmed by both approaches. Oxford Poverty Human Development Initiative relies on data collected in 2004 instead of that relative to 2007, which means that the results of the OPHI approach do not reflect the current level of poverty in Morocco. Measured by the monetary approach according to the national threshold (U.S. $ 2.15 PPP), poverty also declined from 15.3% in 2001 to 8.9% in 2007.
In short, the lack of variables and data on which the OPHI poverty approach is based proves that it cannot substitute objective approaches of international institutions. It is less relevant than the HCP’s approach. In any case, it cannot be used in the ranking of developing countries, unless it is based on the same reference period and unless it is subject to discussions and debate within the ECOSOC’s Statistical Commission, the sole UN body in charge of validating data and statistical methodology, as recommended by the UN Panel on human development indicator.
ANNEXES
Annex 1: HCP multidimensional poverty approach
The measurement of Standard of Living in Morocco is based on dimensional monetary indicators. These indicators measure the consumption and/or income per capita and highlight only financial resources available to households. Other factors determine well-being, in addition to these resources. They are also related to the development of human capabilities (education and health) and sustainable self-protection vis-à-vis social hardships, and to the quality of life represented by housing conditions, environment, security, social equity, gender equality, etc.
The multidimensional approach of the standard of living, based on the monetary and non-monetary attributes of the conditions of life, could be considered as an alternative to the monetary approach; since it takes into account the different quantitative and qualitative dimensions of well-being, and prioritizes those related to the population’s basic needs.
The High Commission for Planning refers to the poverty multidimensional approach precisely in order to remedy to the analytical shortcomings of the one-dimensional monetary indicators. This approach is based on the factorial analysis of the multiple correspondences (ACM) -optimal coding option- to construct a composite index of the standard of living. Aggregating a series of welfare indicators, this index is a multidimensional measurement of the standard of living and multidimensional poverty.
Methodology and data sources
The most appropriate statistical method to calculate the weight of the variables that define a composite index of standard of living is the factorial analysis of the multiple correspondences (ACM) - optimal coding option. It also has the advantage of giving each household a coefficient depending on its position in the first factorial axis (more than 63% of the total inertia). The functional form of this coefficient is defined as follows considering m : index of a given household and cm : its own value :
Where K : number of categorical indicators; Jk : number of modalities of the indicator K; : weight of the category Jk; : binary variable taking the value 1 when the unit (household) has the category Jk and 0 otherwise.
Selection of modalities relative to the key variables of the composite index of the standard of living is based on the ACM applied to the samples in a 'cross section' of consumption surveys in 2001 and the standard of living of households in 1991 and 2007, realized by the High Commission for Planning on representative samples at the national, urban and rural scales.
Results
The main obtained applying ACM are related to the dimensions of multidimensional poverty in Morocco and the evolution of the composite index of the standard of living.
Multidimensional poverty dimensions
The multiple correspondences’ analysis provided the parameters based on the selection of variables involved in the construction of the composite index of the standard of living. The main criterion used to reduce the number of variables without losing the overall substantial consistency is the criterion of the ordinal explicative power of the first factorial axis. The variables that have this property are those that comply with the rule according to which well-being deteriorates from a position of wealth to a situation of poverty throughout this axis.
This axis opposes two household profiles defined by the index of the standard of living: the first one includes the poorest households in terms of the standard of living, including households whose heads of family are illiterate, more than three quarters of their members have no educational abilities, deprived from access to health care and have no medical-health insurance, their children suffer from stunted growth, live in precarious housing units, with no connection to drinking water, electricity and liquid sanitation and equipped with neither basic comfort elements nor household durables, live in poverty in general and suffer from food poverty in particular.
On the other hand, households with the highest composite index of the standard of living are lead by a literate person, their members have access to education and training and have a medical-health insurance, they live in villas, connected to water and electricity networks, have comfort elements and household durables and spend on food alone the equivalent of four times the poverty line. We conclude that households’ budgetary resources are only one component of the standard of living in Morocco and that the condition of being of the population is the result of non-monetary, quantitative and qualitative, factors.
Given the available data and ACM parameters, any composite measurement of the standard of living is more appropriate in the Moroccan context that incorporates the following dimensions , namely:
1. Knowledge: education, training and literacy of the general population and younger generations in particular;
2. health measured by the health and medical coverage and consultation following an illness;
3. healthy and balanced nutrition: supply of drinking water, decent nutrition (according to WHO/FAO standards) for children and adults, and sustainable self-protection from nutrition poverty;
4. ensuring viable environment characterized by living in decent housing units equipped with electricity, liquid sanitation, refrigerator, bath / shower, kitchen, toilet, cooker, etc.;
5. vocational integration: economic activity of households’ members and employment opportunities for young people in particular;
6. social equity and gender equality, measured by the position in the social scale and gender equality in terms of education, training and health care;
7. communication means (TV, mobile phone...) and means of transportation.
In short, only indicators adopted within the framework of MDGs are analytically similar to the Standard of Living Index (INV). This index introduces new dimensions in the evaluation of well-being conditions of. Still, it leaves little other binding or widely distributed dimensions (the case of vaccination). It can be used in all quantitative and qualitative analysis of the living standard, knowing that the level at which it is established (between 8 and 12% in 2007), does not matter vis-à-vis the trend it registers, particularly because of the subjectivity of poverty lines and changes that intervene in the value of the dimensions that make this type of poverty approaches.
Annex 2: Presentation of Oxford Poverty and Human Development Initiative’s multidimensional poverty approach
Methodology:
The index is composed of 10 indicators representing three human development dimensions. Thus, a household is considered to be in deprivation;
• in terms of education:
1. if none of its members completed five years of education.
2. if one of its children in school age does not attend school from the first to 8th grade
• in terms of health
1. if one of its children is deceased
2. if one of its members is malnourished.
• in terms of the standard of living
1. if the household has no electricity.
2. if it does not have access to clean water within 30 minutes of walk from home.
3. if it has no toilet or has a shared toilet.
4. if the floor of its house is dirty, with sand or manure.
5. if it cooks using wood, coal or manure.
6. If it has no car or tractor and does not have at least two of the following items: radio, television, telephone, bicycle, or motorcycle.
Health and education indicators have a weight of 1/6 each and 1/18 for those relative to the standard of living. Thus, each dimension of the three dimensions has a weight of 1/3.
Definitions
• A household is considered poor if the weight of the indicators from which it is deprived exceeds 30%.
• The intensity of poverty is the average number of deprivations.
• The index of multidimensional poverty is the product of poverty rates by intensity.
Results
This methodology has been applied to 104 developing countries for which data exist and came up with a total number of poor people in these countries of 1.7 billion instead of 1.3 calculated by the WB approach, based on $ 1.25 per day.
As far as Morocco is concerned, this approach was applied to the available data of the Demography Health Survey realized among a sample of 12,000 households by the Ministry of Health in 2003/2004 (field collection lasted from October 2003 to February 2004).
Annex 3: Poverty rates of the different multidimensional approaches according to the residence area
In%
Year HCP Approach Oxford Approach
Environment Urban Rural National Urban Rural National
1991 -/- 1992 10,4 55,7 36,5 25,8 84,3 58,3
2001 -/- 2003-04 9,4 42,3 23,9 -- -- 28,5
2006/07 7,4 18,3 12,1 2,8 21,9 11,1
NB: Calculations based on data collected from the national surveys on the standard of living in 1991/92 and 2006/07, the national survey on households’ consumption expenditure in 2000/01, and 2003/04 survey on the demography and health.
Annex 4: The objectives of the Demography and Health Survey (2004)
The National Survey on Population and Family Health (EPSF) conducted in Morocco in 2003-2004 aimed mainly to:
 collect data to calculate demographic rates, particularly rates of fertility and infant and child mortality according to the residence area; urban/rural, and by region;
 collect data on vaccination coverage among children and the coverage of supervised births according to the residence area; urban/rural, and by region;
 measure the rates of contraceptive use by method and according to the residence area; urban/rural, and by region;
 collect data on fertility preferences, including unmet needs regarding to contraception;
 collect data on women’s chronic diseases, family health and reproductive health;
 collect data on the prevalence and treatment of diarrhea and other diseases among children under five years.
Annex 5: Criticisms of the OPHI approach expressed by World Bank’s experts
The MPI is a composite of indicators selected for consistency with the UNDP’s famous Human Development Index (HDI). The HDI uses aggregate country-level data, while the MPI uses household-level data, which is then aggregated to country level. The index has ten components; two represent health (malnutrition, and child mortality), two are educational achievements (years of schooling and school enrolment), and six aim to capture “living standards” (including both access to services and proxies for household wealth). The three broad categories–health, education, and living standards–are weighted equally (one-third each) to form the composite index.
The MPI’s six “living standard” indicators are likely to be correlated with consumption or income, but they are unlikely to be very responsive to economic fluctuations. The MPI would probably not capture well the impacts on poor people of economic downturns (such as the Global Financial Crisis) or rapid upswings in macro-economic performance.
The precise indicators used in the MPI were not in fact chosen because they are the best available data on each dimension of poverty. Rather they were chosen because the methodology used by the MPI requires that the analyst has all the indicators for exactly the same sampled household. So they must all come from one survey. There is much better data available on virtually all of the components of the MPI, but these better data can’t be used in the MPI since they are only available from different surveys. This aspect of their methodology greatly constrains the exercise.
There is a deeper concern about the MPI, which holds even if the best data all came from just one survey. The index is essentially adding up “apples and oranges” without knowing their relative price. When one measures aggregate consumption, one relies on economic theory, which says that (under certain conditions) market prices provide the correct weights for aggregation. We have no such theory for an index like the MPI. A decision has to be taken, and no consensus exists on how the multiple dimensions should be weighted to form the composite index.
As the HDI, the weights chosen by the analyst may be challenged, and may be unacceptable to many people. How can one contend (as the MPI does implicitly) that the death of a child is equivalent to having a dirt floor, cooking with wood, and not having a radio, TV, telephone, bike or car? Or that attaining these material conditions is equivalent to an extra year of schooling (such that someone has at least 5 years) or to not having any malnourished family member? These are highly questionable value judgments. Sometimes such judgments are needed in policy making at country level, but we would not want to have them buried in some aggregate index. Rather, they should be brought out explicitly in the specific country and policy context, which will determine what trade off is considered appropriate; any given dimension of poverty will have higher priority in some countries and for some policy problems than others.
Poverty is indeed multidimensional. But it is not obvious how a composite multidimensional poverty index such as the MPI contributes to better thinking about poverty, or better policies for fighting poverty. Being multidimensional about poverty is not about adding up fundamentally different things in arbitrary ways. Rather it is about explicitly recognizing that there are important aspects of welfare that cannot be captured in a single index.”

Although it is a nice effort but it would be more appreciated, if, instead of writing long thesis on poverty, efforts should be made to defeat poverty. Ways and means should be devised to help poor people around the globe. It would be great cause to fight poverty.

Congratulations to Alkire and Foster for focusing much needed attention on the issue of multidimensional poverty and, moreover, for advancing the case for a single, multidimensional index to measure deprivation in the developing world. As seemingly most development economists recognize, poverty is more than a lack of income or inadequate consumption, but is composed a host of factors that simultaneously act to constrain capabilities and increase deprivation. Part of the debate around unidimensional or multidimensional metrics plays out something like this: from a policy perspective, if poverty is equated with lack of income, then policies that promote economic growth would appear to be all that are needed to reduce poverty. If, instead, poverty is a multidimensional phenomenon, then, as Kanbur and Grusky have put it, “The task (of remediating multidimensional poverty) …. requires targeting those aspects of inequality and poverty (e.g. residential segregation) that are causal with respect to many outcomes and hence likely to bring about cascades of change (my emphasis).” Our task as researchers and policymakers is to determine which aspects of poverty are causal with respect to many outcomes, and to make those aspects the targets of policy interventions.
I do have a few questions, though, for the HDRO, particularly if, as noted in the press release back in June, the MPI will replace the Amartya Sen and Sudhir Anand-developed Human Poverty Index.
In some of the literature that came out in June around the launch of the MPI, it was noted that “the MPI fixes weights between countries to enable cross-national comparisons; alongside this we strongly encourage countries to develop national measures having richer dimensions, and indicators and weights that reflect their context as Mexico did and Colombia is doing.”
Does this mean that the HDRO will calculate its MPI for country X, while country X may calculate its own in any given year? If they differ, will the HDRO’s calculation be the MPI of record, or will the country’s be? What if country X takes advantage of the MPI’s flexibility in the choice of dimensions and indicators and, due to changes in political leadership, for example, chooses to calculate that country’s MPI with a different array of indicators the following year? What does this do to the ability of researchers to calculate change over time? In this case, would researchers simply resort to the UNDP’s calculation of country X’s MPI?
And what about the data sources used to calculate the MPI? Ravallion notes that:
“Rather (the indicators) were chosen because the methodology used by the MPI requires that the analyst has all the indicators for exactly the same sampled household. So they must all come from one survey. There is much better data available on virtually all of the components of the MPI, but these better data can’t be used in the MPI since they are only available from different surveys. This aspect of their methodology greatly constrains the exercise.”
An advantage that the HPI has over the MPI is that it can be used in the absence of disaggregated data. As I understood it, HDRO statisticians collect new or projected data for each of the HPI’s 4 indicators from one year to the next—from the UN Department of Economic and Social Affairs Population Division’s analysis of national vital registration systems, from UNESCO’s Institute for Statistics, from WHO, etc. From a quick review of OPHI’s country profiles, it appears as though the data for individual country MPIs are drawn either from Demographic and Health Surveys (conducted every 5 years), or from the World Health Surveys (conducted irregularly), or from Multiple Indicator Cluster Surveys (conducted every 5 years or so), or… How will the MPI for any given country be calculated next year, given the infrequency of the surveys on which it depends? For those interested in longitudinal studies of changes in deprivation, how should the MPI be used? It would seem that an argument could be made that the loss in precision is made up for in the HPI’s ability to measure annual changes in deprivation, albeit at an aggregated level.
On the question of decomposability, Alkire and Foster tout this feature of the MPI as one of its more significant advantages over other multidimensional indices that rely on aggregated data. However, Sen seems less convinced, noting that when decomposability is insisted on for all possible subgroupings, a basic conceptual problem emerges:
" The mathematical form of decomposability has had the odd result of ruling out any comparative perspective (and the corresponding sociological insights), which is, in fact, fatal for both inequality and poverty measurement… It is easy to see why decomposability has such a strong appeal. It is ‘nice’ to be able to ‘break down’ the overall poverty of a total population into poverty in different subgroups of people that make up the total population. It gives, I suppose, some forensic satisfaction in solving a ‘whodunit’ (and by how much respectively)… (However), mathematically the demand that the breakdown works for every logically possible classification has the effect that the only measures of inequality or poverty that survive treat every individual as an island ....” (Sen, in Grusky and Kanbur, eds, Poverty and Inequality, 2006)
Again, I applaud Alkire and Foster for bringing attention to the measurement of multidimensional poverty. However, readers should be reminded that theirs is only the latest in a long line of similar efforts, each with strengths and weaknesses. The UNDP may want to consider including the MPI to supplement its array of metrics, rather than to supplant the HPI. They measure different, but still important, things.

Here are the comments that I sent as a response to Morocco. Unfortunately footnotes don't transfer, so this is just the text.
Thank you very much for your letters and interest in OPHI’s work and particularly in the new Multidimensional Poverty Index (MPI) that we constructed with the United Nations Development Program’s Human Development Report Office for the 2010 Human Development Report to be launched as an experimental series on 4 November 2010.
The MPI, like any other national poverty measure, is shaped both by constraints of data and technology, and by the purposes of the exercise. The purpose of this exercise was to come up with a measure of multidimensional poverty that could complement income poverty measures with direct measures of deprivation, and provide, like income poverty, a snapshot of acute multidimensional poverty for different data sets.
Our work benefitted greatly from discussions with the HDRO team, with statistical and expert advisors to the UNDP HDRO, as well as with other colleagues including participants in an OPHI workshop in June this year. The research and applications related to multidimensional poverty measurement are developing a new degree of transparency and rigour, and we hope that this exchange can contribute to that. As the series is being introduced on an experimental basis, we do plan to improve and revise as appropriate in response to comments and feedback.
You have sent several sets of comments, which are helpful and I attach a response to each of the points raised.
1. Data Constraints. First, you note that the MPI is limited by data constraints. This is a point stressed repeatedly in my paper with Maria Emma Santos, where we write, “The...binding constraint is whether the data exist. Due to data constraints (as well as, perhaps, interpretability) we have had to severely limit the dimensions. For example, we do not have sufficient data on work or on empowerment” (p 12, section on ‘Choosing Dimensions’).
Data constraints affect not only the dimensions we include – as you mention – but also our ability to measure the existing dimensions (health, education, and standard of living) fully. For example the DHS and MICS surveys are not designed to measure health for all household members, but certain key variables for women of reproductive age, and children (which they do well). Our aim was to make the best possible use of existing data, while also drawing attention to data limitations (as many others have also) and undertaking robustness checks.
OPHI are also committed to measuring non-traditional dimensions of poverty; in fact our other theme of research, called Missing Dimensions, seeks to develop brief survey modules on dimensions such as informal work, safety from violence, empowerment, and social connectedness, because these are often cited as features of poverty by participatory work with poor people, and clearly analysis of these features could enrich the multidimensional poverty literature. Which of these missing dimensions should be incorporated in a multidimensional poverty measure depends upon the purpose of the index as well as the associations among indicators.
2. Dynamic Context. You argue that ‘The variables on which the measurement of the MPI is based become problematic when poverty is put in a dynamic approach’ and give the example of a household who becomes non-poor because an out-of-school child passes the age of 14, so is no longer considered ‘out of school’ because he or she is too old.
This may be a problem if the MPI were used with panel data. However, the DHS, MICS, and WHS surveys that we use are not panel data surveys: that is, they do not follow the same household across different periods in the way you mention, nor do they develop chronic poverty measures to track entries into and out of multidimensional poverty in different periods. Rather, they take a representative sample of the population at each point in time, and look at the relevant population (e.g. school-aged children) at that point in time. Clearly if there were a stark demographic shift (a drop in fertility meaning that in one sample the percentage of children was much lower than in the previous period), this would register in the MPI, but the kinds of shift you mention are not captured by our data sources.
The forthcoming paper by Apablaza, Ocampo and Yalonetzky, which is drawn on in Alkire & Santos section 4.6, studies changes in the MPI for 10 countries across time. As we show in our paper using the cases of Ethiopia, Bangladesh and Ghana, dynamic time series studies of MPI over time can be used to analyse to different features of multidimensional poverty reduction – such as the extent to which intensity of poverty declines as well as the headcount, and which indicators drive poverty reduction (or worsen even though poverty overall goes down). The decomposition of changes allows us to assess the relative importance of changes in the headcount and the intensity in "explaining" the overall change in the adjusted headcount. Also, we can decompose the changes in the headcount to ascertain the main drivers of that change, e.g. whether it was due more to changes in the population composition (e.g. across regions), or to changes in the specific headcounts of the groups into which the headcount has been decomposed. Similarly, in the case of the average deprivations of the poor, i.e. the intensity, it is possible to express the change as a sum of changes in the proportion of the poor who are deprived in each specific dimension or variable.
3. Threshold. You argue that ‘the measurement of poverty according to the Multidimensional Poverty Index is based on a subjective threshold and does not take into consideration, therefore, comparisons with the monetary approach, the threshold of which is determined objectively.’ You also refer to MPI cutoff as arbitrary in constract to income poverty.
It seems that there is some misunderstanding here. The MPI poverty cutoff – which requires people to be deprived in 30% of the dimensions in order to be identified as multidimensionally poor – is not subjective. Subjective poverty lines are set when people are asked how much they require in order not to be poor.
Those working on poverty measurement use the word ‘arbitrary’ in a specialised way which is not its normal usage. A poverty line (both in the income and in multidimensional space) is referred to as ‘arbitrary’ not because it has no normative or objective basis, but rather because it is not theoretically determined. Thus an income poverty line based on a calorie basket is also often referred to as ‘arbitrary’, since it usually uses particular set of equivalence scales, assumes that market prices are right and that each person in each location can convert income into the minimum basket of goods and services required to lead a non-impoverished life.
The standard practice for (arbitrary) income poverty lines is to test how sensitive are the results to the choice of the line, using stochastic dominance tests. Similarly, what we did was to compare the poverty estimate between all possible pairs of countries using a poverty cutoff of 20%, 30% and of 40%. This is detailed in Section 4.8 of the paper. We found that in 95% of the pair wise comparisons, one country has higher (lower) poverty than the other regardless of the poverty cutoff. These results suggest that the particular cutoff of 30% we use for the MPI is not a critical choice that dramatically affects results.
Finally, you compare Morocco to other countries which have higher $2/day income poverty rates and higher inequality. In our view, this is precisely the information that is of interest. The MPI cannot include income because of the fact that income data are not collected in the surveys we use. In these circumstances, it will be useful to study in greater depth than has been done to date countries’ different performances on income poverty and MPI.
4. Time Periods. You write, ‘Data reference periods range from 2000 to 2008, making the classification of countries according to the Multidimensional Poverty Index groundless.’ In fact, we do not classify countries at all. You are absolutely right that the base years differ for the MPI. As we wrote, “although we would have liked to estimate poverty for exactly the same year in all countries to enable a strict cross-country comparison, this was not possible given that the different surveys have been performed in different years in each country.” (page 20). In our countries, 52% of the 5.2 billion people we cover, living in 65 countries have data that is 2005 or later; data for 44% of people living in 31 countries come from 2003 or 2004 (largely due to the World Health Survey 2003 data, which includes China), and data for 3% of people living in 10 countries come from 2000-2002.
Does this make all comparisons groundless? Actually, the use of data from different years is unfortunately common, because poverty surveys are not implemented every year. Base years also differ for the HDI education indicators, the HPI indicators, the MDGs, and income poverty indicators. The methods used to deal with this vary. The 2004 Chen and Ravallion statistics use the closest survey to the reference year (which in their case is a narrower period than ours), and interpolate progress (Chen and Ravallion 2008). We considered this option. Methods have been used to predict changes in individual MDG indicators, including those related to variables we use. However such methods are also subject to criticism and debate, because they are based on a set of assumptions that may be controversial. Thus we decided to use actual data only in this 2010 MPI: not to interpolate between years, nor to use proxies for missing variables (on which please see below). The advantage of doing so is transparency: the years and indicators used can be seen and verified by anyone, and the MPI can be easily re-constructed (as indeed HCP have done) because the datasets as well as our methodologies are publicly available.
The other concern that you raise in this section appears to be misplaced. You argue that in the MPI, “missing indicators are replaced by the lower or higher bounds of variables or their proxies”. This is not accurate. We did not impute any data, nor use any proxies. If a country survey did not include a variable, then the remaining variables for that dimension were used, and the weights were adjusted such that each dimension continued to receive equal weights. As in the case above, we could have chosen a different approach – the techniques exist and are feasible – but, in consultation with the UNDP’s statistical advisors, chose a more direct route instead.
5. More up-to-date Data. In a separate letter, you drew attention to your comparison of the MPI using a 2007 dataset for Morocco and argued that “it is unfair and unjust to release in 2010 reuslts based on data going back to 2003/2004.”
I wrote back immediately requesting access to the dataset mentioned, but have not received a reply. We have also checked thoroughly online but no more up to date dataset is available for Morocco. OPHI are only able to construct multidimensional poverty indices using publicly available data. For example, we are aware that Uganda and Ethiopia also have more up to date DHS surveys, however these were not available to the public when we calculated the MPI hence we used the previous survey data that were available. When new data become publically available we will calculate the updated MPIs.
6. National vs International Measures. Also in your letter to me, you argue that ‘the choice of dimensions and indicators must be based on the preferences and priorities of the population. But this of course depends on the conditions and specificities of the population. This makes international comparisons irrelevant and misleading.’
There is a long and distinguished debate about the appropriateness of internationally – or for that matter nationally – comparable poverty measures, versus more context-specific, participatory measures. Clearly different kinds of measures can be of tremendous value in different contexts, and no one is suited to all purposes. The MPI is an international index, which is seeking to compare very basic aspects of acute multidimensional poverty – malnutrition, child mortality, households with no member having 5 years of education, children out of school, not having electricity, clean drinking water, sanitation, a floor, clean cooking fuel, and not owning more than one of the following assets: television, radio, telephone, bicycle, motorcycle, or refrigerator. Despite its evident limitations, it is of interest that while in some countries over half of the population are deprived in 30% of the weighted indicators, in other countries many fewer people experience this kind of acute poverty and indeed in Slovenia and Slovakia we did not identify any persons as experiencing acute multidimensional poverty. This gives a strong policy message of hope: such acute deprivation can be eradicated. Also while in many countries, like Morocco, the MPI headcount is higher, there are also cases showing that the converse is possible. Indeed this is the case for about one-fourth of the countries including Tanzania and Uzbekistan.
Of course our hope and expectation is that in the coming years the MPI will improve as more data are collected. Alongside this, national governments are developing and will develop national measures which are both more accurate to their context, and which provide appropriate information for an adequate policy response. Morocco has been a leader in this work, with the innovative work of the HCP; Mexico is another country in which the official national poverty measure is now multidimensional. Hence just as alongside the $1.25/day measures countries generate national poverty lines and national poverty measures which are reported in national and international reports, it is possible that a similar evolution will occur with multidimensional poverty measures. Part of our hope is to support and learn from national and subnational initiatives as countries develop robust and transparent measures of multidimensional poverty that are tailored to their own context and that endure over time.

8 dollars a day is the minimum wage! Many earn more than than. And the important is that we never had problems with food, infact we export all kind of vegetables, fruits and fish, of course after fulfuling our needs.
Thanks